ARTIFICIAL INTELLIGENCE AND ELECTROCARDIOGRAPHY: THE ROLE OF NEURAL NETWORKS IN THE EARLY DETECTION OF HIDDEN CARDIOVASCULAR PATHOLOGIES AND THEIR CLINICAL SIGNIFICANCE

Abstract

For over a century, the 12-lead electrocardiogram (ECG) has served as the fundamental diagnostic tool in cardiology for assessing the electrical activity of the myocardium. However, traditional ECG interpretation is limited to visual changes detectable by the human eye, frequently missing cellular-level and early structural micropathologies. In recent years, the integration of Convolutional Neural Networks (CNN) and Deep Learning algorithms has transformed the ECG from a simple diagnostic image into a powerful predictive tool. This article aims to illuminate the key pathophysiological mechanisms of AI-assisted ECG analysis, its high sensitivity in detecting asymptomatic pathologies, and the clinical advantages and iatrogenic risks associated with this technological innovation.